University of Groningen
Is a 100% renewable European power system feasible by 2050?
Zappa, William; Junginger, Martin; van den Broek, Machteld
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Applied Energy
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10.1016/j.apenergy.2018.08.109
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Zappa, W., Junginger, M., & van den Broek, M. (2019). Is a 100% renewable European power system
feasible by 2050? Applied Energy, 233-234, 1027-1050. https://doi.org/10.1016/j.apenergy.2018.08.109
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Applied Energy
journal homepage:www.elsevier.com/locate/apenergy
Is a 100% renewable European power system feasible by 2050?
William Zappa
⁎, Martin Junginger, Machteld van den Broek
Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
H I G H L I G H T S
•
Seven scenarios for a 100% renewable European power system are modelled for 2050.•
A 100% renewable system could operate with the same level of adequacy as today.•
Mass mobilisation of Europe’s solid biomass and biogas resources would be required.•
90% more generation and 240% more transmission capacity would be needed than today.•
Costs would be ∼530 €billion per year, 30% more than a system with nuclear or CCS. A R T I C L E I N F O Keywords: Renewable energy Power system System adequacy Biomass Solar photovoltaic Transmission A B S T R A C TIn this study, we model seven scenarios for the European power system in 2050 based on 100% renewable energy sources, assuming different levels of future demand and technology availability, and compare them with a scenario which includes low-carbon non-renewable technologies. We find that a 100% renewable European power system could operate with the same level of system adequacy as today when relying on European re-sources alone, even in the most challenging weather year observed in the period from 1979 to 2015. However, based on our scenario results, realising such a system by 2050 would require: (i) a 90% increase in generation capacity to at least 1.9 TW (compared with 1 TW installed today), (ii) reliable cross-border transmission capacity at least 140 GW higher than current levels (60 GW), (iii) the well-managed integration of heat pumps and electric vehicles into the power system to reduce demand peaks and biogas requirements, (iv) the implementation of energy efficiency measures to avoid even larger increases in required biomass demand, generation and trans-mission capacity, (v) wind deployment levels of 7.5 GW y−1(currently 10.6 GW y−1) to be maintained, while solar photovoltaic deployment to increase to at least 15 GW y−1(currently 10.5 GW y−1), (vi) large-scale mo-bilisation of Europe’s biomass resources, with power sector biomass consumption reaching at least 8.5 EJ in the most challenging year (compared with 1.9 EJ today), and (vii) increasing solid biomass and biogas capacity deployment to at least 4 GW y−1and 6 GW y−1respectively. We find that even when wind and solar photo-voltaic capacity is installed in optimum locations, the total cost of a 100% renewable power system (∼530 €bn y−1) would be approximately 30% higher than a power system which includes other low-carbon technologies such as nuclear, or carbon capture and storage (∼410 €bn y−1). Furthermore, a 100% renewable system may not deliver the level of emission reductions necessary to achieve Europe’s climate goals by 2050, as negative emissions from biomass with carbon capture and storage may still be required to offset an increase in indirect emissions, or to realise more ambitious decarbonisation pathways.
1. Introduction
In 2011, the European Union (EU) reaffirmed its objective to reduce greenhouse gas (GHG) emissions by 80–95% by 2050 compared to 1990 levels, this being seen as a necessary step to keep global warming below 2 °C in line with the projections of the Intergovernmental Panel on
Climate Change (IPCC) [1]. This was followed in 2016 by the Paris
Agreement to keep warming “well below 2 °C above pre-industrial levels and pursue efforts to limit the temperature increase to 1.5 °C above pre-industrial levels”[2,3]. In order to achieve either of these goals, emis-sions from the power sector must fall essentially to zero, or even turn
negative by 2050[4,5]. This will require large-scale implementation of
low-carbon technologies such as renewable energy sources (RES), nu-clear power, and carbon capture and storage (CCS).
https://doi.org/10.1016/j.apenergy.2018.08.109
Received 13 January 2018; Received in revised form 3 July 2018; Accepted 18 August 2018 ⁎Corresponding author.
E-mail address:w.g.zappa@uu.nl(W. Zappa).
Applied Energy 233–234 (2019) 1027–1050
Available online 09 November 2018
0306-2619/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
For one reason or another, a number of studies have excluded nu-clear and CCS technologies and investigated whether national power
systems could rely on 100% RES, such as those for Denmark[6,7], The
Netherlands [8], Germany[9,10], France[11,12], Ireland[13],
Por-tugal [14], Israel [15], Japan [16], Australia [17], New Zealand
[18,19], the United States[20], or even the whole world[20,21]. Fully renewable scenarios have also been proposed for the whole of Europe in
2050, of which some of the most notable are summarized inTable 1.1
These scenarios are usually developed using energy system models to assess whether projected demand could be met by potential RES supply; however, sufficient RES supply alone does not indicate that a 100% RES power system is feasible as, due to their intermittent generation, vari-able renewvari-able energy sources (vRES) such as wind and photovoltaics (PV) make balancing electricity demand and supply more difficult than
in power systems without vRES[22–25]. In a 100% RES power system,
any residual demand not supplied by vRES must be provided by one of the dispatchable RES generation technologies (hydro, bioelectricity, concentrating solar thermal power (CSP), and geothermal), or storage. However, in the short term, technical limitations mean that it may not be possible for these plants to ramp quickly enough to keep supply and demand in balance, leading to over-voltages or unserved energy in the network. In the long term, some years can be less sunny or windy then others, meaning that wind and PV installations cannot be relied upon to produce the same amount of electricity each year. Therefore, we con-sider that any assessment of the feasibility of a 100% RES power system should include some analysis of both its long- and short-term reliability. Although there is no standard definition, the Council on Large Electric Systems (CIGRE) and the European Network of Transmission System Operators for Electricity (ENTSO-E) define reliability as “the ability of the [power] system to deliver electrical energy to all points of
utilization within acceptable standards and in the amounts desired”
[26,27].2This definition of reliability incorporates two other terms:
system adequacy, the ability of the power system to supply the re-quired power and energy requirements subject to outages and opera-tional constraints; and system security, the extent to which a power system can withstand sudden disturbances (ibid.). Assessing the relia-bility of power systems is one of the objectives of power system mod-elling (PSM).
In their assessment of the feasibility of 100% RES power systems
based on a review of 24 studies, Heard et al.[42]found no consistent
definition for feasibility, and instead based their assessment on whether studies: (i) performed simulations using PSM to ensure that supply could meet demand reliably, (ii) assumed demand levels consistent with mainstream forecasts, (iii) identified the necessary transmission and distribution requirements, and (iv) considered the provision of ancillary
services. Meanwhile, in their critique of Heard et al., Brown et al.[43]
refuted several of their feasibility criteria as being surmountable at minimal cost, instead arguing that “how to reach a high share of renew-ables in the most cost-effective manner while respecting environmental, so-cial and political constraints” is the key issue. Thus, while there is no agreement in the literature on the definition of power system feasibility, achieving a reliable and cost-effective system seems a fundamental requirement.
Only two ([32,35]) of the studies presented in Table 1 were
Nomenclature
bn billion (109)
AD anaerobic digestion
BECCS bioenergy with carbon capture and storage
CAES compressed air energy storage
CAPEX capital expenditure
CCS carbon capture and storage
CDDA Common Database on Designated Areas
CIGRE Council on Large Electric Systems
CLC Corine Land Cover
COP coefficient of performance
CSP concentrating solar power
DNI direct normal irradiance
DSM demand-side management
ECF European Climate Foundation
ECMWF European Centre for Medium-Range Weather Forecasts
EEA European Environment Agency
EEZ Exclusive Economic Zone
ENTSO-E European Network of Transmission System Operators for Electricity
ERA-Interim European Reanalysis Interim Dataset
ETRI Energy Technology Reference Indicators
EU European Union
EV electric vehicle
FOM fixed operating and maintenance costs
GDP gross domestic product
GHG greenhouse gas
HP heat pump
HVAC high-voltage alternating current
HVDC high-voltage direct current
IDC interest during construction
IPCC Intergovernmental Panel on Climate Change
JRC European Union Joint Research Centre
LoLE Loss of Load Expectation
LT long term
MENA Middle-East and North Africa
MILP mixed-integer linear programming
NAO North-Atlantic oscillation
NPV net present value
OCC overnight capital cost
OCGT open-cycle gas turbine
OECD Organisation for Economic Co-operation and Development
OPF optimal power flow
PHS pumped hydro storage
PSM power system modelling
PV photovoltaic
RES renewable energy source
RoR run-of-river hydro
ST short term
STO storage hydro
TCR total capital requirement
TYNDP Ten-Tear Network Development Plan
UCED unit commitment and economic dispatch
VOM variable operating and maintenance costs
vRES variable renewable energy source
WACC weighted average cost of capital
1Several other scenario studies have also been published for the European power system in 2050[49,54,127–130], but these are not discussed as they report insufficient technical detail, or do not approach 100% RES.
2ENTSO-E uses this definition in the Continental Europe Operation Handbook (2004)[27], however more recently, ENTSO-E defines another term – security of supply – as “the ability of a power system to provide an adequate and
secure supply of electricity in ordinary conditions“ which is similar to reliability
[131]. The main distinction between system adequacy and security is that se-curity refers to the short-term operation of the power system (e.g. resilience to generator outages, transmission faults), whereas adequacy refers to long-term operation. System adequacy can also be divided into generation adequacy, and transmission adequacy[26].
Table 1 Total electricity generation and installed capacity in several studies featuring high-RES European power systems. For comparison, data for the current (2015) power system is also shown, as well as the European Commission Joint Research Centre’s (JRC) Reference Scenario for 2050. Study Reference portfolios High-RES Scenarios Current situation (2015) [28,29] JRC EU Reference Scenario [30] EU Energy Roadmap 2050 [31] Roadmap 2050 [32] Energy Revolution (5th Edition) [33] Re-thinking 2050 [34] e-Highway 2050 [35] Scenario (year) – 2050 High RES (2050) 100% RES (2050) Advanced (2050) 2050 100% RES X-7 (2050) Geographical scope EU28 + NO + CH EU28 EU28 EU27 + NO + CH (+North Africa for CSP) OECD Europe (+North Africa for CSP) EU27 EU28 + NO + CH + Balkans + North Africa Final demand (TWh y −1) a 2912 h 3574 3377 4385 g 3889 (6020) e – 4385 Model(s) used l – PRIMES PRIMES Undisclosed MESAP/Planet Undisclosed Antares PSM performed? – – No Yes Partly k No Yes Generation portfolio Generation (TWh/y) Capacity (GW) Generation (TWh/y) Capacity (GW) Generation (TWh/y) Capacity (GW) Generation (TWh/y) Capacity (GW) Generation (TWh/y) Capacity (GW) Generation (TWh/y) Capacity (GW) Generation (TWh/y) Capacity (GW) Onshore wind n 310 136 980 368 2504 612 758 245 1450 594 1552 462 2238 875 Offshore wind 373 758 190 901 237 PV 102 95 429 295 843 603 900 815 1080 926 1347 962 743 i 675 CSP 6 m 2.3 m 0 0 0 0 980 b 203 b 1050 f 208 f 385 96 278 i 58 Ocean c 0.5 m 0.2 m 0 0 0 30 0 0 160 53 158 65 0 0 Biomass 119 25 391 57 494 163 587 85 467 108 496 100 454 184 Geothermal 6 m 0.8 m 14 4 31 4 343 b 47 b 390 52 601 77 0 0 Hydro 536 194 421 142 396 131 591 205 620 223 448 194 890 297 Natural gas 409 217 836 269 386 182 144 215 0 0 0 0 13 73 Coal 888 187 252 52 108 62 0 0 0 0 0 0 0 0 Oil 32 32 5 4 0 19 0 0 0 0 0 0 0 0 Nuclear 836 125 737 93 180 41 0 0 0 0 0 0 0 0 Hydrogen d – – 0 0 200 0 0 0 267 181 0 0 0 0 Total RES 1008 (31%) 404 (40%) 2235 (55%) 866 (67%) 4267 (83%) 1916 (86%) 4917 (97%) 1790 (89%) 6118 (96%) 2401 (93%) 4987 (100%) 1956 (100%) 4603 (100%) 2088 (97%) of which vRES j 412 (13%) 231 (23%) 1409 (35%) 662 (52%) 3347 (65%) 1618 (73%) 2416 (48%) 1250 (62%) 3591 (56%) 1810 (70%) 3057 (61%) 1489 (76%) 2981 (65%) 1549 (72%) Total Non-RES 2241 (69%) 608 (60%) 1828 (45%) 418 (33%) 874 (17%) 304 (14%) 144 (3%) 215 (11%) 267 (4%) 181 (7%) 0 (0%) 0 (0%) 13 (0%) 73 (3%) Total 3249 1012 4064 1283 5141 2220 5061 2005 6385 f 2582 f 4987 1956 4616 2162 Abbreviations: CSP – Concentrating solar power, EU – European Union, PSM – Power system modelling, PV – Photovoltaic, RES – Renewable energy source, vRES – variable renewable energy source, aExcluding grid losses and own consumption in electricity generation sector. bECF’s 100% RES scenario was based on an 80% RES scenario, increased to 100% RES by adding 15% CSP generation from North Africa and 5% from Enhanced Geothermal technologies. cIncludes wave, tidal and all other forms of marine energy. dHydrogen is reported as non-renewable in this table for clarity as even though some studies assume hydrogen is 100% renewable (e.g. [33] ), it is not always clear. eFinal consumption (3889 TWh) reported does not include 1924 TWh of demand for hydrogen production, or 207 TWh for synfuel production. Once included, total final consumption is 6020 TWh. fTotal installed capacity (2460 GW) and generation (5764 TWh) reported in the original study for OECD Europe do not include an assumed import of 620 TWh y −1 from north African CSP, so CSP capacity increased to compensate for this by assuming the same capacity factor for North Africa CSP as for European CSP in the study (55%). gCalculated from total reported demand of 4900 TWh y −1,including 10.5% grid losses as assumed in original study. hBased on Eurostat data for EU28 and NO, Swiss final consumption (58.2 TWh) from the Swiss Federal Office of Energy [36] . iOnly aggregated generation from PV and CSP of 1021 TWh was reported in this study, disaggregated here by assuming a 55% capacity factor for CSP. jConsidering wind, PV and ocean power as variable renewable energy sources (vRES). Run-of-river (RoR) hydro capacity could also be considered vRES, however not all studies indicate the share of RoR capacity. kModelling studies were performed by Energynautics on an earlier (2009) edition of the Energy Revolution report [37,38] .This included transmission but, judging from published information, did not model detailed generator flexibility constraints. No evidence of detailed modelling of the most recent edition (5th) of the Energy Revolution report could be found. lPRIMES is an energy system model developed by the E3MLab at the National Technical University of Athens [39] ,it is not a detailed power system model. MESAP and PlaNet are energy system and network planning models originally developed by the University of Stuttgart but now maintained by Seven2one [40] .Antares is a sequential Monte-Carlo power system simulator developed by RTE [41] . m Current contribution is so small that it is not reported specifically by ENTSO-E, thus the value is taken from Eurostat instead but not included in the total [29] . nWhen the breakdown between onshore and offshore wind is unavailable, the total (onshore + offshore) is reported under onshore wind.
supported by detailed PSM simulations, which revealed additional
portfolio requirements.3Several other studies have also investigated a
high-RES European power system using either PSM or another
model-ling approach [32,33,35,38,44–54]. However, even including these
studies, we identify several common limitations – in addition to those
raised by Heard et al.[42]and Brown et al.[43]– which leave doubts
about the feasibility of a 100% RES European power system:
•
Dispatchable thermal generator flexibility limitations are notin-cluded, meaning backup and balancing requirements may be
un-derestimated (e.g.[32,35,46–51]).
•
Bioelectricity is treated crudely using one fuel or generationtech-nology, or without considering regional differences in supply
po-tentials and costs (e.g.[32,38,49,50]).
•
Simulations are run for only a single arbitrary (e.g. [44,50]) orseveral weather years (e.g.[38,47,53]) which does not guarantee
system adequacy in the worst year.
•
Studies rely on significant capacities from technologies such as CSP,geothermal, seasonal storage or biomass, which currently show few
signs of growth (e.g.[32,34,35,55]).
•
Studies allocate vRES capacity exogenously to locations or countrieswith the highest capacity factors (e.g.[32,38,52]). Within countries,
capacity is allocated based on currently exploited sites (e.g. [46,47]), or averaged across all sites (e.g. [48,50,53]). However, these approaches ignore the possibility that insufficient suitable land or sea area may be available to support the assumed level of vRES deployment, leading to optimistic aggregated generation profiles. Furthermore, simply allocating capacity ignores the po-tential to reduce costs by optimising the spatial distribution of vRES along with transmission.
•
A fixed capacity credit is assumed for vRES technologies, whereas inreality this varies with both location and time.
•
Significant electricity is imported from the Middle East and NorthAfrican (MENA) countries (e.g. [32,33,35,38,49,54]). While still
renewable, it could be considered misleading to label a European power system 100% renewable if it relies on significant imports of electricity from outside Europe.
•
The power system is modelled at some point in the future (e.g.2050), without considering whether the transition from the current system and expansion of renewable capacity is practically
achiev-able (e.g.[50]).
In this study, we aim to get insights into the feasibility of a 100% RES European power system in 2050 without these shortcomings by building a model of the power system in which dispatchable generators and their flexibility limitations are modelled in detail. By including the spatial deployment of vRES directly in the optimisation, land avail-ability is accounted for explicitly, and vRES generation profiles are consistent with their spatial deployment. We model seven scenarios for a 100% RES European power system to explore the impact of un-certainties in future demand, technology development, and compare the costs with one scenario of a non-RES power system. Lastly, we use long-term weather data and detailed hourly simulations to assess system adequacy. Given the lack of consensus on the definition of power system feasibility, we instead attempt to answer the following more concrete questions, leaving the final verdict of feasibility to the
reader:
•
Could a future 100% RES European power system be supplied usingEuropean resources alone, and have the same level of system ade-quacy as today’s power system?
•
What is the most cost-effective portfolio of RES generation andtransmission network capacity?
•
How do the costs of a 100% RES European power system comparewith a power system which includes non-RES technologies?
•
Could the transition to a 100% RES power system be made by 2050?Our study is structured as follows. First, we outline our overall
ap-proach in Section 2, which is underpinned by significant input data
(Section2.2). Based on the results presented in Section3, we discuss the
implications of our study in Section 4. Finally, we offer some
con-cluding remarks in Section5(seeTable 2).
2. Method
Our model of a 100% RES power system is built using the PLEXOS
modelling package (Section2.1).4After supplying the necessary input
data and assumptions (Section 2.2) and defining several scenarios
(Section2.3), we run a long-term (LT) capacity expansion optimisation
to determine the least-cost portfolio of generation technologies and transmission infrastructure investments which can meet demand
re-liably (Section2.4.1). The optimised portfolio for each scenario is then
simulated at hourly resolution using detailed unit commitment and economic dispatch (UCED) calculations, to ensure that demand can be
met in the short term (ST) (Section2.4.2). An overview of our method is
given inFig. 1. Extensive appendices supporting our work can be found
in thesupplementary material available online.
2.1. Build model
PLEXOS is a mixed-integer linear programming (MILP) model which has been used in several studies on RES integration and system
adequacy (e.g.[50,56–58]). By coupling its LT Plan and ST Schedule
modules, PLEXOS can be used to perform both capacity expansion (i.e. building new generation and transmission infrastructure) and UCED calculations, considering power plant flexibility limitations and flexible loads. The objective function of the LT Plan is to mini-mise the total net present value (NPV) of build costs, fixed operation and maintenance (FOM) costs, and variable operating and
main-tenance (VOM) costs [59], while the objective function of the ST
Schedule is to minimise generation costs. A more detailed explanation of the PLEXOS software can be found in other published works (e.g.
[57]). We take the geographical scope of Europe in our study as the
EU28 countries as well as Switzerland and Norway, as shown in Fig. 2.5
Unlike traditional thermal generators, spatial considerations play a vital role in modelling vRES generators as their location determines not only the available resource, but also how much capacity can be de-ployed in a given area. To account for this, we introduce the spatial distribution of vRES capacity directly into the optimisation by coupling
3For example, in[32]it was found that in addition to the base RES capacity (1790 GW), 215 GW of natural gas turbines would be required for back-up and balancing. However, accounting for this gas use reduced the share of RES to 97%, and meant the target of 95% decarbonisation was not met. While noting that this natural gas could be replaced by biogas or renewable hydrogen, the consequences and costs of these options were not fully explored. Thus, ne-glecting analysis with PSM leaves uncertainty as to whether the system would actually work, whether emission reductions can really be achieved, and at what cost.
4PLEXOS (version 7.2) is developed by Energy Exemplar and is available fromhttp://www.energyexemplar.com/.
5Despite the UK’s decision in 2016 to leave the EU, we include the UK in this study as for grid stability and economic reasons, it would be in both the EU’s and the UK’s interests for the UK to remain well-integrated with continental Europe. However, this would require finding solutions to various legal issues and coming to an agreement with the EU[132]. Furthermore, the UK remains committed to decarbonisation and scenarios published by National Grid in 2017 show significant increases in offshore wind and interconnection capacity by 2050[133]. While part of the Continental European network, we exclude the Balkan states due to a lack of data.
the PLEXOS model with a high-resolution spatial grid.6This grid is
based on a regular 0.75° × 0.75° grid, modified to respect national
boundaries [60], exclude protected conservation areas [61], and
re-stricted to offshore water depths of up to 50 m within the Exclusive
Economic Zone (EEZ) of each country[62]. We combine the spatial grid
with the Corine Land Cover (CLC2012) dataset[63,64]and European
Reanalysis Interim (ERA-Interim) weather dataset [65], in order to
determine both the amount of suitable land area for vRES deployment,
and the weather conditions at each location.7These are used to define
the maximum installed capacity and generation profiles for wind and PV per grid cell.
2.2. Input data and assumptions 2.2.1. Electricity demand
As a starting point, we take the hourly historical electricity demand
for each country for the year 2015 from ENTSO-E[66]. To account for
potential electrification of the heating and transport sectors by 2050,
for our Base demand profile we add additional demand of 500 TWh y−1
for heat pumps (HPs) and a further 800 TWh y−1for electric vehicles
(EVs), based on the levels from ECF’s Roadmap 2050 study which as-sumes almost complete electrification of passenger vehicles, and
sig-nificant uptake of HPs[32].
We consider two additional demand profiles to account for un-certainty in future demand. For the High Demand profile, we scale up
the Base profile to match the 2050 demand of 6020 TWh y−1 from
Greenpeace’s Energy [R]evolution scenario[33]. For the Alternative
De-mand profile, we take the Vision 4 deDe-mand profile from ENTSO-E’s Ten-Year Network Development Plan (TYNDP) for 2030 and scale it up to
4409 TWh y−1so that total demand matches the Base profile total, but
the hourly profile itself is smoother.8Further details are provided in the
supplementary material.
2.2.2. Generation technologies
We consider a broad portfolio of RES generation technologies in-cluding wind (onshore and offshore), PV (utility and rooftop),
bioe-lectricity, CSP, geothermal, and hydro power.9The main
techno-eco-nomic assumptions for all generator types are given inTable 3. In order
to compare the costs of a 100% RES power system with a non-RES
power system,Table 3also includes techno-economic parameters for
selected natural gas and coal generation technologies (with and without
CCS), nuclear, and bioenergy with CCS (BECCS).10Due to model
lim-itations, seasonal storage (e.g. power to gas) is not considered. For consistency, most costs are taken from the European Commission Joint Research Centre’s (JRC) Energy Technology Reference Indicator (ETRI)
projections for 2010–2050[70]. As investments for a 100% RES power
system by 2050 would need to be made before 2050, we take the costs for 2040. A uniform weighted average cost of capital (WACC) of 8% is used to annualise investment costs in new generation and transmission
capacity.11A brief explanation of how each technology is modelled is
provided below.
Hourly generation from wind farms is estimated by combining wind speed profiles from ERA-Interim with commercial wind turbine power curves. ERA-Interim is also used as the source of solar radiation data to model both PV and CSP. Solar PV is modelled with efficiencies of 21%
and 17% for rooftop and utility-scale systems respectively[71,72]. CSP
generators are modelled as solar tower plants equipped with two-axis-tracking heliostats, and eight hours of molten salt thermal storage at nominal load. By calculating the maximum suitable area for wind and PV deployment per grid cell, and limiting how much is available for each technology, we allow PLEXOS to optimise the spatial deployment
of wind and PV capacity.12
We consider two bioelectricity technologies: biomass fluidised bed
Table 2
Demand profile parameters.
Demand Profile Source profile Modifications Demand
Minimum (GW) Maximum (GW) Annual (TWh) Underlying source demand
profiles Actual 2015 demand fromENTSO-E[66] – – 230 504 3109
TYNDP 2016 Vision 4[67] – – 266 563 3616
Modelled demand profiles Basea Actual 2015 demand from
ENTSO-E HPs: +500 TWh y
−1
EVs: +800 TWh y−1 241 889 4409
High Demand Base Scaled up to 6020 TWh y−1 329 1214 6020
Alternative Demand TYNDP 2016 Vision 4b Scaled up to 4409 TWh y−1 324 686 4409 a Demand increases from HPs and EVs are taken from[32]. This study assumed that 90% of building heat demand could be met by HPs by 2050 (including HPs in district heating systems), assuming an average coefficient of performance (COP) of 4. For EVs, the study assumed almost complete electrification of passenger vehicles by 2050. Assuming an average EV efficiency of ∼140 Wh km−1(Tesla Model 3) and mileage of 15,000 km y−1per vehicle, 800 TWh y−1would be sufficient to cover approximately 370 million passenger EVs in 2050. While this is significantly more than the current fleet of 260 million passenger vehicles[68], 800 TWh y−1 would allow for continued growth in the European fleet (which could reach 370 million by 2050 if the current average growth rate of 1.1% y−1is maintained), or partial electrification of light and medium commercial vehicles.
b Based on published information, the Vision 4 profile assumes increasing total demand, full implementation of smart-grid technology, large-scale adoption of HPs and EVs with flexible charging and generation (∼10% vehicle fleet), and large-scale adoption of HPs (∼9% heat demand)[69].
6Built using the software ArcGIS Pro from ESRI.http://www.esri.com/. 7ERA-Interim is a global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) covering 1979 to the present (2017), and includes 3-hourly data on wind speed, solar radiation, and temperature[65,134]. The spatial grid in this study is built to match that of ERA-Interim, which has a resolution of 0.75° × 0.75° (approximately 50 km).
8The Vision 4 profile was developed for the year 2030 assuming lower pe-netration of HP and EVs than our Base profile, and includes the effects of smart EV charging and other demand-side technologies which the Base profile does not.
9We exclude ocean (tidal and wave) energy[135,136]and osmotic power (derived from salinity gradients)[137]as their slow growth makes it unlikely for them to produce significant amounts of electricity by 2050.
10Even though BECCS uses renewable biomass, we consider any technology employing CCS as non-renewable as while the European potential for CO2 storage is significant (∼117 Gt CO2), it is finite[138].
11The cost of capital can vary significantly between countries and between technologies[139]. We choose 8% as a common value used in similar studies, assuming that perceived risks for renewable investments are likely to fall in the future[140]. This is higher than the reference financial and social discount rates of 3–5% recommended by[140,141].
12Assumed availability is taken from the literature, ranging from 1% for utility PV on arable land to 20% for offshore wind on open water. Further details are given in thesupplementary material.
METHOD
Input data
Supply input data and assumptions
Build model
Define scenarios
Perform model runs for 2050
In
pu
t da
ta
Re
su
lts
Process results Electricity demand profiles Generator parameters Fuel availability and costs Reserve requirements Demand response potential Transmission system data Long-term capacity expansion optimisation Biomass utilisation Short-term economic dispatch simulation Generation portfolio Transmission requirements Spatial vRES distribution Total system costs Sensitivity analysis System adequacy
Fig. 1. Overview of the method used in this study.
Fig. 2. Countries and transmission lines
con-sidered in this study. Countries are labelled ac-cording to ISO 3166 except for Greece (EL) and the United Kingdom, which is split into Northern Ireland (NI) and Great Britain (GB). Transmission of electricity occurs between the notional load centres of each country using a centre of gravity approach (see Section2.2.3). The spatial grid is shown in blue, which includes all land and offshore areas within the exclusive economic zone (EEZ) of each country, up to a maximum water depth of 50 m for offshore wind. Countries not included in the study are shaded grey. The regions are only used to describe re-sults, each country is modelled individually.
Table 3 Assumed techno-economic parameters for generation technologies in 2040. Generator type Nominal unit size (MW) Base CAPEX a(€ kW −1) FOM b(% y −1) VOM c(€ MWh −1 ) Nom. efficiency (%) d Lifetime (y) c Build time (y) e LCOE f(€ MWh −1) Renewable technologies Wind Onshore – 1320 1.9% 0 – 25 1 67 Offshore – 2610 2.8% 0 – 25 1 94 PV g Rooftop – 950 2% 0 22% 25 < 1 82 Utility – 600 1.7% 0 17% 25 1 51 Hydro h Run-of-river (RoR) 70 5720 1.5% 5.0 87% 60 – 214 Storage (STO) 100 2840 1% 4.0 87% 60 – 105 Pumped storage (PHS) 400 2840 1.5% 4.0 76% 60 – 105 Bio-electricity Biomass fluidised bed (Bio-FB) 300 2450 1.8% 3.9 38% 25 3 112 Open-cycle biogas turbine (Bio-OCGT) 100 l 600 3% 11.2 42% 25 1 23 Concentrating solar power (CSP) i 50 4930 4% 8.1 40% 30 2 119 Geothermal 50 4780 2% 0 24% 30 3 64 Non-renewable and other technologies j Natural gas Open-cycle natural gas turbine (Gas-OCGT) 100 600 3% 11.2 42% 30 1 290 Natural gas combined cycle (Gas-NGCC) 580 1000 2.5% 2.0 63% 30 3 106 NGCC with CCS (Gas-NGCC-CCS) 485 1860 2.5% 4.1 56% 30 4 96 Coal Pulverised coal plant (Coal-PC) 750 1980 2.5% 3.7 47% 40 4 151 PC with CCS (Coal-PC-CCS) 630 3300 2.5% 5.6 41% 40 5 110 Bio-FB with CCS (Bio-FB-CCS) k 255 4060 1.8% 5.9 28% 25 4 44 Nuclear (3rd generation) 1500 5330 1.7% 2.6 33% 60 7 77 Abbreviations: CAPEX – Capital expenditure, CCS – Carbon capture and storage, FOM – Fixed operating and maintenance costs, LCOE – Levelised cost of electricity, NGCC – Natural gas combined cycle, PV – Photovoltaic, VOM – Variable operating and maintenance costs. Note: All costs given in €2016 using historical Eurozone inflation rates unless otherwise stated [77] . aBase CAPEX represents the total capital requirement (TCR), comprising the overnight capital cost (OCC) in 2040 taken from JRC ETRI 2014 [70] (including grid connection cost), and interest during construction (IDC). b FOM costs given as a percentage of OCC taken from the ETRI [70] . cJRC ETRI 2014 [70] . dEfficiencies and part-load performance are mostly taken from Brouwer et al. [50] .More details can be found in the supplementary material . eConstruction time is used to calculate IDC using an 8% discount rate. Taken from [78] apart from nuclear, which is based on more recent data [79] .Plants built within one year have no IDC. IDC is not included for hydro plants as we include no new capacity. fLCOE shown for comparison only, assuming 8% discount rate, average fuel costs and indicative capacity factors of 25% for onshore wind, 38% for offshore wind, 15% for PV, 30% for hydro, 90% for nuclear and geothermal, 5% for gas turbines, and 60% for the remaining technologies. gFuture PV cost estimates vary widely in the literature. For this reason, we include both rooftop (< 100 kW) and utility (> 2 MW) scale installations to account not only for their different spatial constraints, but also to include a range of investment costs as an implicit cost sensitivity. Efficiency is based on commercial monocrystalline silicon modules for rooftop PV [71] ,and polycrystalline modules for utility PV [72] . hFor hydro, nominal size is based on average plant size per category from ENTSO-E [66] .For hydro-PHP plants, we assume a reservoir size of 45 GWh per plant (4.5 days) based on the mean calculated specific reservoir size of 113 GWh GW −1 for existing hydro-PHP plants from ENTSO-E data. As the cost of Hydro-STO plants depends on capacity, we use an average of the costs for plants between 10–100 MW and > 100 MW [70] .The cost for Hydro-STO is used for Hydro-PHS, as the source does not distinguish between plants equipped with reversible turbines, or plants with dedicated pumping capacity. In any case, hydro capacity is exogenous in all scenarios and the costs do not affect the optimisation. Once-through turbine and pumping efficiency are both taken as 87% [50,80] . iCSP plant cost includes 8 h of molten salt thermal storage per plant [81] .The peak efficiency of the CSP power block component is 40%, based on electricity generation and total heat input. Overall CSP plant efficiency (output electricity with respect to DNI) is approximately 17% [82] .Most CSP plants are in the order of 200 MW, consisting of several smaller units of around 50 MW each [83] . jFuel costs of 7 € GJ −1,2 € GJ −1 and 1 € GJ −1 are taken for natural gas, coal and nuclear fuel respectively based on the 2 Degree Scenario (2DS) for 2050 from the IEA’s ETP2016 [84] .A CO 2 price of 120 € t −1 is assumed from the IEA’s 2015 World Energy Outlook (WEO) 450 Scenario for 2040 [85] .We assume a uniform CO 2 capture rate for CCS technologies of 90% [70] ,and CO2 transport and storage costs of 13.5 € t −1 CO 2 [50] . kNo data could be found for Bio-FB-CCS plants, which instead are estimated based on differences between ETRI reported values for Coal-PC with and without CCS: 60% higher CAPEX, 16% lower nominal capacity, 10% (absolute) lower efficiency, and 53% higher VOM than the Bio-FB (non-CCS) plants. lBiogas plants are typically small units (< 1 MW), operating on either gas engine or gas turbine technology. However, modelling with such small units can lead to numerical instabilities. Thus, we use a higher nominal plant size of 100 MW, the same as Gas-OCGTs.
combustion (Bio-FB) plants and open-cycle gas turbines (Bio-OCGT), which are supplied by three categories of biomass fuels (biogas sub-strates, solid woody biomass and solid waste biomass), based on country-specific cost-supply curves for 14 different biomass feedstocks
[73].13We assume that solid biomass is combusted in Bio-FBs, while
biogas substrates – after conversion to biogas – are combusted in
Bio-OCGTs. Raw biomass fuel costs range from 1.4 € GJ−1to 14.4 € GJ−1
depending on the fuel type and country of origin, with a total domestic
supply potential of 10 EJ y−1in 2050[73].14For biogas substrates, an
additional cost of 10.4 € (GJ substrate)−1is included for the conversion
by anaerobic digestion (AD) to raw biogas for local use, and a further
3.2 € GJ−1for upgrading to biomethane and injection into the gas grid
[74]. In order to avoid infeasible solutions, biomass supply is modelled
as a soft constraint by allowing the model to draw on additional bio-mass, albeit at significantly higher cost.
Run-of-river (RoR) and storage (STO) hydropower capacity is ag-gregated per country using a nominal unit size, with annual capacity
factors limited to historical levels[29,66]. Pumped hydro storage (PHS)
capacity is also aggregated for each country but the storage is modelled explicitly, assuming an average storage volume of approximately five
days at nominal load.15
While wind turbines and PV panels can in principle be located al-most anywhere, hydro and geothermal power plants require sites with specific geological features, and CSP plants should be installed in lo-cations which receive high direct normal irradiance (DNI). For these reasons, the installed capacity and spatial distribution of hydro, geo-thermal and CSP are specified exogenously:
•
We assume that total hydro capacity in 2050 remains unchangedfrom today at approximately 200 GW, with the same geographical distribution and split between RoR (31%), STO (48%) and PHS
(21%) capacity[75].
•
Geothermal capacity is set at 50 GW to reflect deployment levelsassumed in previous high-RES studies [32,33] (seeTable 1), and
allocated to countries in proportion to their economic geothermal
potential[76].
•
CSP capacity is fixed at 200 GW, reflecting levels found in the mostambitious high-RES scenarios[32,33](seeTable 1). However, many
of these studies locate considerable CSP capacity in the MENA countries where higher annual DNI levels are available. In order to
fit this capacity into Europe, we allocate CSP capacity to grid cells in order of decreasing DNI, while adjusting both the minimum allowed DNI and assumed availabilities of suitable land classes until 200 GW is reached – with a preference for sparsely inhabited areas to minimise impacts on local communities. As a result, CSP is allocated
to grid cells with average DNI levels of 1600 kWh m−2y−1 or
higher, located mostly in Spain (158 GW), Portugal (22 GW), Italy (16 GW), Greece (5 GW) and Cyprus (0.8 GW). Thus, the availability of land for CSP is not taken as a hard constraint as for PV and wind, but indicates the area which would be required to accommodate 200 GW of CSP in Europe.
The firm capacity for all dispatchable generators is taken as 90%, assuming 5% unavailability due to unplanned outages, and a further 5%
for planned maintenance[86].16The firm capacity for vRES
technolo-gies is estimated per grid cell following the approach of Milligan[87]as
the average capacity factor during the peak 1% of demand hours per year. As a result, PV receives a capacity credit of zero in all grid cells, onshore wind has a median capacity credit of 12%, and offshore wind a
median of 10%. Further details can be found in the supplementary
material.
2.2.3. Transmission
We use a ‘centre-of-gravity’ approach to model transmission flows between countries, with the urban-area-weighted centres of each
country serving as nodes (seeFig. 2). Taking the existing capacity in
2016 as a starting point[66], new transmission capacity can be built if
this lowers total costs, based on the costs given inTable 4. Subsea lines
are assumed to be high voltage direct current (HVDC), while land-based lines are high voltage alternating current (HVAC). Transmission and distribution within countries is modelled as copper plate.
For the wind and PV technologies, we also estimate the amount of grid reinforcement required to bring this electricity to the main trans-mission grid by calculating the shortest transtrans-mission distance (across either land or sea) to the nominal load centre of the country in which it is deployed, and add the cost of this additional transmission to the base
CAPEX fromTable 3.17
2.2.4. Demand response
Demand response, also known as demand side management (DSM), is the willingness of electricity consumers to shift or even curtail their
load during times of peak system residual demand[95]. In this study,
we consider 16 GW of load shedding capacity from heavy industrial processes, and 82 GW of load shifting capacity from various commercial and residential appliances based on the technical potentials reported by
Gils[95], and assumed deployment levels (as a percentage of technical
potential) from Bertsch et al.[96]. Demand shedding costs vary from
100 € kWh−1to over 2000 € kWh−1depending on the industry, which
is activated whenever electricity prices exceed these levels. Limits are imposed on the volume and activation of residential and commercial
DSM, depending on the appliance and the season.18
2.2.5. Reserves
Power systems require operating reserves in order to balance out 13Currently, most large-scale bioelectricity plants in Europe are the result of
the partial (e.g. co-firing) or complete conversion of existing pulverised coal plants to biomass. However, as many existing coal plants will have been de-commissioned by 2050, we do not consider the conversion of existing plants. Instead, we model future large-scale biomass as fluidised bed combustion plants as their projected 2050 costs are similar to coal plants with added costs for biomass co-firing (based on[70]). The alternative would be to assume future biomass plants use more efficient integrated gasification combined cycle (IGCC) technology (as done by[142]), however these are approximately 40% more expensive, potentially less flexible, and no large-scale units are currently op-erating.
14We do not include sugar, starch and oil crops (which we reserve for liquid biofuel production), roundwood fuel wood (which we reserve for firewood), nor black liquor. We include the transport of solid woody biomass between countries[143], while for practical reasons we assume that solid waste biomass must be used in its country of origin.
15Based on an in-house database of Europe’s 120 largest hydro plants and their associated reservoirs incorporating data from various open-source data-bases (e.g.[80,144,145]), we calculate average specific reservoir sizes of 60, 1608 and 113 MWh MW−1for RoR, STO and PHP plants respectively. Multi-plying these values by the average plant sizes from ENTSO-E[66]gives total European hydro storage capacity of approximately 160 TWh. This total matches quite well with the 180 TWh reported by [75]. Also, the value of 60 MWh MW−1for RoR storage shows that most RoR plants also have several hours of storage, and thus capable of some level of dispatchability[146]. The resulting 56 GW of PHS capacity is equipped with 6.4 TWh of storage.
16Firm capacity, also known as capacity credit or capacity value, represents the contribution a generator makes to system adequacy. Put simply, it indicates the share of installed capacity which can be relied upon during times of peak demand. For dispatchable generators, a value of 90% is typical and allows for forced and unforced offline periods. While CSP generation depends on inter-mittent sunlight, the capacity credit of CSP plants can exceed 90% when equipped with at least four hours of storage, thus we assume this value[147]. 17These notional ‘reinforcement lines’ are not modelled explicitly as part of the transmission network, and only serve to include the cost of bringing elec-tricity from more remote vRES sites to load centres.
mismatches between demand and generation due to (i) demand forecast errors, (ii) vRES generation forecast errors, and (iii) unplanned
gen-erator outages[97]. In this study, we include fast-responding spinning
reserves (both up and down regulation) available within five minutes, as well as standing reserves available within one hour. We assume a single Europe-wide reserve market in which all generation technologies
are capable of providing reserves, including wind and PV.19
2.3. Define scenarios
We consider eight scenarios in order to understand the impact of assumptions made in this study and uncertainties involved in modelling
a future 100% RES power system (Table 5). Seven of these scenarios
focus on uncertainty in final demand and technological developments in a 100% RES power system, while the eighth scenario includes non-RES capacity as explained below:
•
In the Base scenario, all RES technologies are modelled aspre-viously explained with CSP, geothermal and hydro capacities spe-cified exogenously, assuming the Base demand level of
4409 TWh y−1.
•
In the High Demand scenario, demand is scaled up by 36% to6020 TWh y−1keeping the underlying demand profile the same, to
see the impact of further growth in demand20;
•
In the Alternative Demand Profile scenario, we test how sensitiveour results are to the base hourly demand profile by using the less peaky ‘Vision 4’ hourly demand profile from ENTSO-E, scaled up to
the Base annual demand (4409 TWh y−1);
•
In the No CSP or Geothermal scenario, we exclude these twodis-patchable technologies to see how critical their future deployment is for a fully renewable European power system;
•
In the No Biomass scenario, we do not allow any power generationfrom biomass, reflecting possible social opposition to the tech-nology, or complete prioritisation of biomass for other end-use sectors (e.g. heating, chemicals, industry);
•
In the Storage scenario, we allow the model to build additionalgrid-scale storage capacity in the form of compressed-air energy storage
(CAES)21;
•
In the Free RES scenario, we specify no exogenous CSP orgeo-thermal capacity and leave the model free to optimise all RES ca-pacity (excluding hydro); and
•
In the Allow non-RES scenario, we allow all low-carbon (but notnecessarily renewable) technologies to be built, so that the costs of a fully renewable system can be compared with one which includes non-renewable alternatives.
2.4. Perform model runs
With hydro, geothermal, and CSP the only technologies exogenously defined, we first run PLEXOS’ LT Plan module in order to find the cost-optimum deployment of the remaining generation capacity and
transmission investments which can reliably meet demand (Section 2.4.1). Then, we test how this system performs at hourly resolution by running detailed UCED calculations with the ST Schedule module
(Section2.4.2).22
2.4.1. Long-term capacity optimisation
One aspect of system adequacy is ensuring that enough generation capacity is available to meet demand reliably. Ideally, this would in-volve optimising the generation portfolio and transmission network considering all available weather data (i.e. from 1979 to 2015) si-multaneously, to ensure that the risk of short supply is acceptable even in the most challenging weather year, and that the generation portfolio is not sensitive to any individual year. However, optimising the in-stalled capacity of two biomass and four vRES technologies across more than 2000 grid cells – for 37 years of weather data – is not feasible with available computing power. Furthermore, due to the model complexity, it is not amenable to probabilistic methods. Thus, we take the simpler approach of deterministically optimising capacity for the most chal-lenging weather year experienced by Europe in the period 1979–2015. Based on the historical data, we determine 2010 as the year with the overall lowest potential wind and PV generation, and run the capacity
expansion optimisation for this year (see thesupplementary material
for additional details). In performing the capacity expansion optimisa-tion, we make the following assumptions:
•
Europe is modelled as a single integrated power system in whichcapacity can be shared between countries.
•
Apart from a reference level of transmission (60 GW) and hydroplant capacity (200 GW), we take Europe as a clean slate and include no legacy generation capacity. Nor do we consider any government policies which may preclude technologies in any given country.
•
Transmission is modelled as simple active power transport, ratherthan a full optimal power flow (OPF) problem.23
Table 4
Techno-economic parameters for HVAC and HVDC transmission infrastructure.
Component CAPEX FOMc(%
CAPEX y−1) Lossesd(% 100 km−1) Lines (€ MW−1 km−1) Substations/ Converters (€ MW−1) HVACa Overhead 330 38,800 3.5% 0.7% Underground (Direct buried) 3370 0.45% HVDCb Subsea 240 121,000 3.5% 0.35% Note: All costs given in €2016unless otherwise stated. Abbreviations: CAPEX – Capital expenditure, FOM – Fixed operating and maintenance costs. A lifetime of 40 years is assumed for all transmission system components. A 6% outage rate is assumed for transmission lines, with a mean time to repair of 14 h [88,89].
a Based on a study for the UK [90], specific costs range from 333 to 605 € MW−1km−1for overhead HVAC lines and 3370 to 4780 € MW−1km−1 for direct-buried lines respectively, depending on the line length and carrying capacity. The quoted values correspond to a double circuit 400 kV 75 km line with 6930 MVA carrying capacity. Given we consider mainly long-distance transmission, we assume a 90%/10% split between onshore overhead lines and underground cables. HVAC converter costs taken from[91].
b A complete HVDC line includes the cable length and two converter stations. HVDC line and converter costs taken from[91].
c Annual FOM costs equivalent to 3.5% of the base CAPEX[70,90]. d HVAC losses taken from[92], HVDC losses from[93]. We also include losses of 0.65% per HVDC converter station (average of values from[92,94]).
19Several countries already require that wind farms must be able to supply primary (and in some cases secondary) reserves, which is possible by operating in de-loaded mode or being equipped with storage capacity (e.g. flywheels) [148]. PV plants can also provide primary reserves[149].
20This is to match demand in the Energy [R]evolution study (seeTable 1), which assumed extensive use of electricity for the production of hydrogen for use in other sectors[33].
21We assume storage investment costs of 700 € kW−1(including 8 kWh of storage for every kW capacity installed), round trip efficiency of 63%, FOM costs of 35 € kW−1y−1
, and lifetime of 35 years, based on[50]. Equivalent to 88 € kWh−1, we acknowledge this is rather optimistic given expectations for grid-level storage costs are 340 USD kWh−1 (290 € kWh−1) in 2040 [150]. However, with this scenario we want to see the potential role of storage in the power system at a given cost, not provide an accurate cost assessment.
22The commercial optimisation package Gurobi[151]is used to solve the system of MILP equations generated by PLEXOS.
•
Generator flexibility parameters and operational reserves are notconsidered.24
•
To ensure comparability with the 100% RES scenarios, in the Allownon-RES scenario we constrain total GHG emissions to 45 Mt y−1in
2050.25This represents a reduction of 96% compared with 1990, the
level required to ensure that the EU goal of reducing total CO2
emissions by 80–95% by 2050 can be achieved[4,98,99].
In assessing system adequacy, most countries allow for a Loss of
Load Expectation (LoLE) between 3 h y−1(e.g. BE, GB, FR) and 8 h y−1
(e.g. NI, IE, PT)[100]. However, in our study it is not possible to target
such a specific LoLE level as we cannot include reserve requirements in
the LT Plan, and our vRES firm capacity estimates are not perfect.26
Assuming that each country must have sufficient capacity to cover its peak demand – provided either by its own generators or exchange with neighbouring countries – we instead increase the capacity margin in each country until no unserved energy is observed in the LT Plan
re-sults.27
2.4.2. Short-term hourly dispatch
With the optimum generation portfolios and transmission networks determined from the LT Plan, we then perform detailed UCED simula-tions for each scenario with PLEXOS’ ST Schedule module for the same weather year 2010, including both generator flexibility constraints and operating reserve requirements. Simulations are run at hourly
resolu-tion for one typical week per month, in order to reduce soluresolu-tion time.28
In assessing system adequacy, we consider a maximum acceptable level of unserved energy of 0.0003% of total annual demand, based on the expected unserved energy for Europe’s electricity system in 2020 from
ENTSO-E’s 2016 Mid-Term Adequacy Forecast[88].
3. Results
3.1. System adequacy
Based on the results of the LT Plan optimisations, feasible solutions are found for all scenarios, with the exception of the No Biomass sce-nario. This shows that with CSP, geothermal, and hydro capacity at their assumed levels, and in the absence of seasonal storage, a 100% RES power system is not feasible without biomass. Hence, we do not
consider this scenario any further.29 After simulating the remaining
scenarios at hourly resolution, feasible solutions are found with less than 0.0003% unserved energy. From this, we conclude that a 100% RES European power system can achieve the same level of system adequacy as today’s power system.
Table 5
Scenario runs performed.
Scenario Varied parameters
Demand profile Available technologiesa
(O: optimised, X: excluded)
Hydrob CSP Geo vRESc Biod CAES Non-RESe
Base Base (4409 TWh y−1) 200 200 50 O O X X
High demand High demand (6020 TWh y−1) 200 200 50 O O X X
Alternative demand profile Alternative demand (4409 TWh y−1) 200 200 50 O O X X
No CSP or Geothermal Base 200 X X O O X X
No Biomass Base 200 200 50 O X X X
Storage Base 200 200 50 O O O X
Free RES Base 200 O O O O X X
Allow non-RES Base 200 O O O O X O
a A numerical value indicates the exogenous specified capacity in GW. Transmission is freely optimised in all scenarios. DSM is also included in all scenarios; however, demand shedding (16 GW) is included in both the LT Plan and ST Schedule modules, while demand shifting (82 GW) is only included in the ST Schedule runs to minimise computational time.
b Includes STO, RoR and PHS hydro. c Includes all wind and PV technologies. dIncludes Bio-FB and Bio-OCGT.
e Includes all ‘Non-renewable and other technologies’ listed inTable 3.
(footnote continued)
each node, and would provide better estimates for losses and reactive power compensation requirements (e.g. capacitor banks, synchronous condensers) [152,153]. However, this is beyond the scope of our paper.
24For computational reasons, the LT plan does not simulate each hour. Instead, we slice the year into 12 monthly blocks and optimise based on a simplified 12-step load duration curve in each block. As a consequence, chronology is only maintained between the blocks and not within them, thus ramping constraints are not considered[59]. However, both generator flex-ibility and reserves are included in the ST Schedule.
25We assume direct GHG emission factors of 56, 101 and 100 kg CO2equivalent GJ−1(NCV) for natural gas, coal and biomass fuels re-spectively[154]. Note that emissions for biomass are only considered when coupled with CCS in Bio-FB-CCS plants to calculate sequestered CO2. Otherwise, biomass is considered carbon-neutral as we assume that sufficient new biomass is grown (and CO2absorbed) to offset that which is burned.
26Reserve requirements depend on vRES generation profiles, which are not known until after the LT Plan is solved. The vRES capacity credit is estimated based on the hours in which total European-wide demand is highest, however these hours do not necessarily coincide with the peak demand hours in each country. For this reason, we must ensure that some over-capacity is included in the LT Plan so that sufficient capacity is available to cover both reserve re-quirements and vRES firm capacity inaccuracies in the subsequent hourly si-mulations.
27Due to the temporal simplifications and relaxation of some constraints in
(footnote continued)
the LT Plan, some unserved energy is often observed in the hourly ST Schedule results, even if none appears in the LT Plan. Thus, several iterations are usually required increasing the capacity margin until the level of unserved energy in the ST runs is acceptable. This required a capacity margin of around 8%. Even if this approach is rather conservative and more capacity is installed by the model than actually required, this capacity will come in the form of OCGTs (the cheapest capacity providers) which ultimately contribute a relatively minor amount to total costs (see Section3.7).
28Performing hourly simulations for one week for one scenario can take more than 4 h to solve using integer programming with a target MIP gap of 0.1%. Thus, simulating a full year could take more than 200 h, or more than 60 days for all eight scenarios. Instead, we limit the solver time to two hours and solve only the first week of each month.
29We also attempted another scenario excluding biomass but including daily (8 h) storage; however, this also returned an infeasible solution.
3.2. Generation portfolio
The optimised generation portfolio for each scenario is shown in Fig. 3, whileFig. 4 shows the annual generation. All 100% RES sce-narios show a significant expansion of generation capacity compared to today, with total installed capacity ranging from 1.9 TW in the Alter-native Demand Profile scenario to 3.1 TW in the High Demand scenario. Aside from the higher assumed demand, this increase in capacity is due to the low capacity credit of wind and PV, which must be backed up by dispatchable capacity. With the capacity of geothermal, CSP and hydro set exogenously in most scenarios, the only remaining dispatchable RES technology is biomass, which is installed in significant quantities. Compared to the Base scenario, allowing non-RES technologies in the portfolio in the Allow non-RES scenario reduces the size of the total portfolio to 1.4 TW, primarily due to the rollout of some 200 GW of dispatchable zero-carbon nuclear capacity, and 200 GW of Gas-NGCC capacity. Approximately 50 GW of Bio-FB-CCS capacity is also installed as the net negative emissions it generates allow this lower-cost Gas-NGCC capacity to be included in the portfolio without CCS.
In the 100% RES scenarios, onshore wind deployment ranges be-tween 50% (Base) and 64% (No CSP or Geothermal) of its maximum potential (543 GW). Due to its higher cost, offshore wind deployment is modest in most RES scenarios at about 17% of its maximum potential (754 GW); however, deployment increases when demand is higher or CSP is excluded from the portfolio. With 65% (Base) to 85% (High Demand) of its total potential deployed (895 GW), utility PV represents the largest share of installed capacity in all 100% RES scenarios – de-spite making no contribution to firm capacity. Due to its higher cost, rooftop PV is only installed in appreciable amounts in the High Demand and No CSP or Geothermal scenarios, once the best utility PV sites are exploited.
Turning to the dispatchable technologies, biomass plays a critical role in providing peak and load-following capacity in all 100% RES scenarios. This is evidenced by comparing the installed Bio-OCGT ca-pacities in the Base (∼470 GW) and Alternative Demand Profile (∼220 GW) scenarios, showing that with a lower peak demand and smoother demand profile, Bio-OCGT capacity is approximately 50% lower in the Alternative Demand Profile scenario. Meanwhile, Bio-FBs provide between 160 GW and 230 GW of load-following capacity in the 100% RES scenarios. When CSP capacity is optimised, only 38 GW of CSP is installed in the Free RES scenario and no capacity at all is in-stalled in the Allow non-RES scenario. By contrast, geothermal capacity is fully exploited in all scenarios as, with lower VOM costs and a higher capacity factor, it is more competitive than CSP.
At the assumed cost of 700 € kW−1(88 € kWh−1), just under 80 GW
of CAES is installed in the Storage scenario, which displaces an equivalent amount of Bio-OCGT capacity. Total installed generation capacity increases by 30 GW (mostly PV) compared to the Base scenario in order to provide additional electricity for charging the storage, as there is no surplus (curtailed) vRES generation in any scenario which
can be used to charge the storage.30
3.3. Spatial capacity distribution
Fig. 5shows how the optimised generation capacity from the Base scenario is deployed across Europe. For the spatially optimised vRES
technologies (wind and PV), Fig. 6 shows how this capacity is
distributed within each country. Onshore wind capacity is mainly in-stalled in countries bordering the North and Baltic Seas in a band stretching from the British Isles to the Baltic countries. These locations are preferred due to their favourable wind speeds, and central location in Europe which minimises transmission losses. Offshore wind is mainly installed in the North Sea due to the higher wind speeds, and central location. PV capacity is spread across most countries. Within countries, PV capacity is typically installed either in southerly locations, or close to the load centre to reduce costs. Less utility PV capacity is installed in the Iberian Peninsula than might be expected, as much of the suitable land area for vRES is needed to accommodate the exogenous CSP ca-pacity. Furthermore, any additional PV capacity in this region would further increase the transmission needs between Spain and France (see
Section2.2.3).
3.4. Transmission requirements
The optimised transmission grid reinforcements (on top of the
re-ference capacity of 60 GW) for each scenario are shown inFig. 7. In the
scenarios including the 200 GW exogenous CSP capacity, reinforce-ments range from 321 GW to 416 GW, as the transmission corridors FR-ES, FR-DE, FR-BE, and IT-FR must be significantly reinforced to bring CSP generation from the Iberian peninsula to the rest of Europe. However, when CSP capacity is optimised in the Free RES scenario, reinforcements fall to 142 GW due to the more optimal (lower) de-ployment of CSP. Thus, the exogenously defined CSP capacity has a
significant impact on the configuration of the transmission network.31
Very little additional transmission is built in the Allow non-RES scenario due to the lower vRES capacity and absence of CSP. In the Storage scenario, transmission reinforcements fall by 10 GW (3%) compared to the Base scenario. Thus, large-scale transmission expan-sion appears more cost effective than utility-scale daily (8 h) energy storage in balancing supply and demand, even when assuming opti-mistic reductions in future storage costs.
One consequence of a fully interconnected power system is that the reliability of transmission becomes critical for ensuring system ade-quacy as, with a higher dependence on generators in neighbouring countries, the reliability of generators depends not only on availability, but also on the reliability of the transmission lines which deliver their electricity.
3.5. Hourly dispatch
Fig. 8shows the results of the ST Schedule hourly dispatch from the Base scenario for a typical summer week, whileFig. 9shows the hourly dispatch for a typical winter week. Comparing these two figures, we find that:
•
Geothermal, Hydro-STO and Hydro-RoR provide baseload capacitythroughout the year due to their high investment but relatively low marginal cost.
•
Variable PV and wind generation fluctuates hourly, daily, andsea-sonally. While PV can usually be relied upon for significant daytime generation in summer, wind production is less reliable. While
average wind generation tends to be higher in winter,Fig. 9shows
30We observe no curtailment in our study as with both transmission and vRES siting optimised simultaneously, transmission bottlenecks are avoided, and it is more cost-effective to balance the portfolio with firm non-vRES ca-pacity than install additional vRES caca-pacity. Furthermore, as we do not model the transmission or distribution grids within countries, any internal bottlenecks requiring vRES curtailment are neglected. Also, we model and optimise the system for the worst weather year available.
31If this CSP capacity was instead located in the MENA countries, the re-quired network topology would be similar as HVDC connections bringing electricity to central Europe from the MENA countries must go through Spain, Italy, or Greece. For example, the SAPEI HVDC cable linking Sardinia with mainland Italy is the deepest in the world, with some sections reaching 1650 m [93], while significant portions of the Mediterranean Sea exceed 2500 m depth. Attempting to lay cables at greater depths involves significant technical and cost limitations which are unlikely to be overcome before 2030, leaving only 20 years for a trans-Mediterranean HVDC network to be developed[155,156]. This leaves Spain, Italy or Greece as the only alternatives.
that there can be periods of low wind generation, even in winter.
•
CSP plays a significant role in covering night-time demand duringthe summer, but cannot provide the same level of coverage in winter due to the lower DNI received.
•
Biomass plays quite different roles in summer and winter. Insummer, Bio-FB and Bio-OCGT capacity is cycled daily in order to meet peak evening demand, once generation from PV and CSP has ceased. In winter, Bio-FBs are used to provide baseload capacity while day- and night-time peaks – mainly caused by EVs – are met by Bio-OCGTs.
•
DSM is used extensively to both shift and curtail demand duringpeak evening hours, particularly in winter. Hydro-PHS plays a si-milar role tas Bio-OCGTs in providing flexible peak generation during evening hours, especially during summer when electricity from PV can be used for pumping during the day.
Due to the imperfect forecasting of demand and vRES generation, operating reserve requirements in a 100% RES power system with a high vRES share would be significant. An example of this is given in Fig. 10, which shows the provision of operating reserves by each
gen-erator type during the same typical summer week as shown inFig. 8.
Spin-up reserves are mainly provided by hydro and CSP, with Bio-OCGTs providing the majority of stand-up reserves. Down-regulation reserves are provided mainly by CSP and vRES, though practically all technologies contribute some down-regulation during the year. 3.6. Biomass utilisation
Total demand for biomass in the 100% RES scenarios ranges from 8.5 EJ in the Base scenario up to 12.9 EJ in the High Demand scenario. In 2015, Europe produced approximately 5 EJ of biomass for energy
Fig. 3. Installed capacity of each technology per
scenario in 2050, based on weather year 2010 (the lowest PV and wind supply). For compar-ison, the current (2015) installed capacity is also shown with coal, natural gas, PV and biomass shown as Coal-PC, Gas-NGCC, Rooftop PV, and Bio-FB respectively, based on ENTSO-E data [66]. Demand shedding capacity of 16 GW is not shown. The peak total system demand in each scenario is indicated by the ‘●’ symbols (left axis) and the share of vRES capacity indicated by the ‘×’ symbols (right axis).
Fig. 4. Total generation by technology per
sce-nario in 2050, based on weather year 2010 (the lowest PV and wind supply). For comparison, the current (2015) generation is also given with coal, natural gas, PV (total), wind (total) and biomass shown as Coal-PC, Gas-NGCC, Rooftop PV, Onshore Wind and Bio-FB respectively, based on ENTSO-E data[66]. The share of vRES generation is indicated by the ‘×’ symbols (right axis).
